The path to autonomous vehicle (AV) ubiquity on public roads and highways has been highly experimental across several entity types, such as educational institutions, automobile manufacturers, and high technology business entities. AV testing is currently converging upon necessary hardware—such as sensor and computational resources, required for adequate safety of AV operations on public roads—as well as continuously advancing software development in areas of perception, object classification, path prediction, control input responses (e.g., steering, braking, and acceleration inputs), and the like. However, monetization of AV technology has been limited to a gradual progression of autonomy features on offered vehicles manufactured by certain automakers—from active cruise control features to lane-keeping, following, and automated parking and braking features developed by certain vehicle manufacturers.
In the year 2016, human deaths attributed to motor vehicles in the United States reached 40,000 mainly due to speeding, impaired driving, and increasingly distracted driving. It is widely accepted within the automotive and scientific communities that advanced driver-assistance systems and autonomous driving will tremendously reduce vehicle-related accidents and deaths. In addition, wasted time and productivity costs attributed to lengthy commutes may also be significantly reduced or largely eliminated once self-driving vehicle technology becomes ubiquitous in urban sprawls. However, widespread acceptance of autonomous vehicles can only be achieved through proven, real-world results in terms of logged mileage and an indisputable and convincing safety track record.